Chem-Bio Informatics Journal
Online ISSN : 1347-0442
Print ISSN : 1347-6297
Original
Application of TensorFlow to recognition of visualized results of fragment molecular orbital (FMO) calculations
Sona SaitouJun IijimaMayu FujimotoYuji MochizukiKoji OkuwakiHideo DoiYuto Komeiji
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JOURNALS FREE ACCESS

2018 Volume 18 Pages 58-69

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Abstract

We have applied Google’s TensorFlow deep learning toolkit to recognize the visualized results of the fragment molecular orbital (FMO) calculations. Typical protein structures of α-helix and β-sheet provide some characteristic patterns in the two-dimensional map of inter-fragment interaction energy termed as IFIE-map (Kurisaki et al., Biophys. Chem. 130 (2007) 1). A thousand of IFIE-map images with labels depending on the existences of α-helix and β-sheet were prepared by employing 18 proteins and 3 non-protein systems and were subjected to training by TensorFlow. Finally, TensorFlow was fed with new data to test its ability to recognize the structural patterns. We found that the characteristic structures in test IFIE-map images were judged successfully. Thus the ability of pattern recognition of IFIE-map by TensorFlow was proven.

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